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Summary
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Choosing a Project
- Topic? Sports, Beer, Other
- Supervised or unsupervised learning?
- Data Source: Download, Web Scrape, Social Media
- Tools: Python, R, Weka, Tableau, Excel
Choice
- Data from Kaggle
- Audio Analysis
- Supervised Learning
- Classification
- ML in Python
- Presentation & Report in R Markdown
- Excel for results transfer
Goals
- Classify audio clip subjects gender
- Learn what features best seperate gender in audio
- Look for other potential clusters within the data
Method
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Exploration
- Read the data into R/Python
- Ran summary functions on features
- Plotted the data
- Look for patterns and relationships between features
- Determine what features seperate gender best
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Classification
- Used Scikit-learn in Python
- Split the data for training/testing (1/3, 2/3)
- Used gridsearch to identify the best parameters
- KNN (K-Nearest Neighbors)
- Decision Tree (DT)
- Suport Vector Machine (SVM)
- Observed prediction outcomes. Could do better.
- Attempt to Improve on initial results
- KNN: Transform data with PCA
- Decision Tree: Use multiple trees with Random Forest
- SVM: Transform data with PCA
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Review
- Confusion Matrix
- Overall Accuracy Scores
- Male Accuracy
- Female Accuracy
- Parameter Influence
- Graph Results
Overview
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Description
Dataset Comments- Database created to identify a voice as male or female, based upon acoustic properties of the voice and speech.
- The dataset consists of 3,168 recorded voice samples, collected from male and female speakers.
- The voice samples are pre-processed by acoustic analysis in R using the seewave and tuneR packages, with an analyzed frequency range of 0hz-280hz (human vocal range).
- The samples are represented by 21 different features
- Source: Voice Gender Data
Definitions
Sample
EDA
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Classes
Distributions
Boxplots
T Test
Heatmap
Scatter Plot
3D Plot
KNN
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K-Nearest Neighbors
Summary
- Used untransformed data
- Better then a dumb classifier
- Manhattan Distance produced better CV results (p=1)
- Distance weights outperformed uniform weights
- Better at identifying males
- algorithm = auto; n_neighbors = 11; p = 1; weights = distance
Confusion Matrix
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Results
Decision Tree
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Decision Tree
Summary
- Used untransformed data
- MeanFun & Q25 account for +75% of feature importance
- Better at identifying males
- Tree
- criterion = entropy; max_depth = 15; presort = FALSE; splitter = random
Confusion Matrix
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Results
SVM
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Support Vector Machine
Summary
- C = 5.89999999999999
Confusion Matrix
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Parameters
Results
Log Reg
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Logistic Regression
Summary
- C = 0.2; penalty = l1
Confusion Matrix
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Parameters
Results
Random Forest
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Random Forest
Summary
- Best overall accuracy using raw data
- criterion = entropy; max_depth = 14; n_estimators = 14
Confusion Matrix
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Parameters
Results
KNN (PCA)
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K-Nearest Neighbors (PCA)
Summary
- algorithm = auto; n_neighbors = 3; p = 1; weights = distance
Confusion Matrix
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Results
SVM (PCA)
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Support Vector Machine (PCA)
Summary
- C = 9.5
Confusion Matrix
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Parameters
Results
Log Reg (Normal)
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Logistic Regression (PCA)
Summary
- C = 0.35; fit_intercept = FALSE; penalty = l1
Confusion Matrix
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Parameters
Results
Conclusions
Criteria
Discuss criteria. Females hard to get right. Males easier.
ROC
Inital models were bad. Transforming data and using alternate algorithms improved performance.